Within the past decade, the advent of world-wide electronic communications systems has enhanced the way in which people can send and receive information. In particular, the capabilities of real-time video and audio systems have greatly improved in recent years. In order to provide services such as video-on-demand and video conferencing to subscribers, an enormous amount of network bandwidth is required. In fact, network bandwidth is often the main inhibitor in the effectiveness of such systems.
In order to overcome the constraints imposed by networks, compression systems have emerged. These systems reduce the amount of video and audio data which must be transmitted by removing redundancy in the picture sequence. At the receiving end, the picture sequence is uncompressed and may be displayed in real-time.
One example of an emerging video compression standard is the Moving Picture Experts Group ("MPEG") standard. Within the MPEG standard, video compression is defined both within a given picture and between pictures. Video compression within a picture is accomplished by conversion of the digital image from the time domain to the frequency domain by a discrete cosine transform, quantization, and variable length coding. Video compression between pictures is accomplished via a process referred to as motion estimation and compensation, in which a motion vector plus difference data is used to describe the translation of a set of picture elements (pels) from one picture to another.
The ISO MPEG-2 standard specifies only the syntax of bitstream and semantics of the decoding process. The choice of coding parameters and tradeoffs in performance versus complexity are left to the encoder developers.
One aspect of the encoding process is compressing a digital video image into as small a bitstream as possible while still maintaining video detail and quality. The MPEG standard places limitations on the size of the bitstream, and requires that the encoder be able to perform the encoding process. Thus, simply optimizing the bit rate to maintain desired picture quality and detail can be difficult.
For example, a bit rate is defined in bits per second. Based on the frame rate and type of picture being encoded, a number of bits per picture is assigned. At 6,000,000 bits per second (6 Mbps), and pictures at 30 picture frames per second, each picture would be allocated 200,000 bits assuming that the bits are allocated uniformly. With a 720.times.480 picture having 1350 macroblocks, this translates into 148 bits allocated per macroblock. Thus, in the case of scene changes and action videos, the bit rate can be quickly consumed with drastic changes between macroblocks and/or between frames. Picture quality and detail can suffer as a result.
Video compression requires advanced techniques to achieve the highest amount of compression, while still obtaining a desired picture quality. Variable bit rate (VBR) encoding is an option in video compression that allows for each compressed picture to have a different amount of bits based on complexity of intra and inter-picture characteristics. For example, a scene with simple picture content (such as a color test pattern) will require significantly less bits to encode than a scene with complicated picture content (such as a crowded city street) in order to obtain desired picture quality. Because of the amount of information that is needed to characterize the video and the complexity of the algorithms needed to interpret the information to effectively enhance the encoding process, VBR encoding is conventionally accomplished in a non-real-time, two or more pass encoding process. In a first pass, statistics are gathered and analyzed, and in a second pass, the results of the analysis are used to control the encoding process. Although this produces high quality encoding, it does not allow for real-time operation.
This invention seeks to monitor or predict picture quality of a partially encoded video sequence within an encoder in order to optimize picture quality in real time within the video encoding system.